Urban taxi demand prediction faces a critical resolution paradox: high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility, while low-resolution predictions sacrifice responsiveness for stability. We present a Scalable SpatioTemporal Zero-Inflated Poisson Graph Neural Network (SSTZIP-GNN), that resolves this paradox through three innovations: (1) Zero-Inflated Poisson (ZIP) integration that explicitly models structural zeros in sparse demand distributions, distinguishing genuine low-demand periods from data artifacts; (2) Adaptive spatiotemporal learning that dynamically adjusts kernel dilation factors and graph diffusion rates across temporal resolutions using Diffusion Graph Convolutional Networks (DGCNs) and Temporal Convolutional Networks (TCNs); (3) Multimodal feature fusion incorporating real-time crowd-sourced mobility data, socioeconomic indicators, and Global Position System (GPS) trajectories for enhanced robustness under variable urban conditions. Extensive evaluation on 130 million real-world mobility records demonstrates superior performance, achieving 34.8% Mean Absolute Error (MAE) reduction over state-of-the-art baselines. The model reduces computational costs by 46.3% compared to ensemble approaches while maintaining high accuracy across resolutions, delivering 33.4%−53.3% Root Mean Square Error (RMSE) reduction across different prediction resolution scenarios. This unified framework enables cities to implement demand-responsive fleet management, dynamic pricing, and sustainable mobility planning across diverse urban landscapes.